Channel estimation in a large scale Multiple Input Multiple Output (“MIMO”) system is a very challenging task because of the increased dimensions and associated increased complexity that arises therefrom. A large scale multi user MIMO system includes a first plurality of antennae at the base station in communication with one or more receivers that can be single or multiple antenna mobile terminals.
Large scale MIMO communication can occur in cloud RAN (C-RAN) settings, where several collocated base stations are pooled together to form a single, virtual site with many antennae, typically much more than that number of users that are in communication with the site. The advantage to such a system model lies in the increased spatial dimension, whereby base stations can use the system on the uplink to jointly detect several users using simple linear detection methods such as matched filter, zero-forcing, or MMSE, so long as the channel estimate is sufficiently accurate. Similarly, on the downlink, the large number of antennae at the base station can be effectively used to “beam form” signals to targeted users.
Channel estimation is an important concern in this type of system. One approach is to perform channel estimation jointly for a plurality of users. So long as the system remains determined, this approach can provide improved channel estimation and increased efficiency as compared to single user channel estimation. However when using joint channel estimation the system can become under-determined as the number of users increases, so that it becomes impossible to accurately estimate all of the user channels jointly using conventional channel estimation algorithms such as block least squares.
One approach for dealing with underdetermined systems of this type is to use compressive sensing channel estimation. This approach is based on the idea that the number of degrees of freedom of the channel matrix is smaller than its large number of free parameters, so that a sufficiently low rank approximation of the channel matrix can be found which can be conveniently used to solve the channel estimation problem using any of several existing algorithms.
Recent efforts have attempted to solve this channel estimation problem in large scale multiuser MIMO systems by exploiting a prior knowledge of channel sparsity that originates from assuming a finite scattering model of the channel. However, it has been unclear how these compressed sensing channel estimation methods could be applied in a typical cellular communications network operating according to the Long Term Evolution, (“LTE”) standard, especially when the network is operating in a C-RAN setting.
Large scale multi user MIMO is likely to be deployed within networks having a C-RAN architecture and operating according to the LTE standard. In this this type of architecture, the LTE systems are likely to be based on time division duplex (“TDD”) communication. In TDD LTE systems, channel state information is estimated in the uplink based on Sounding Reference Signal (“SRS”) training signals that are included in the uplink specifically for this purpose. Estimated channel reciprocity is then used to perform linear beam forming or precoding on the downlink. Using a comb pattern and careful cell ID selection, a relatively large number of users can be multiplexed to send SRS training signals in the uplink on the same sub-frame, as is required for large scale multi user MIMO communication.
As in many other wireless network environments, time domain least square joint channel estimation will be able to provide sufficiently accurate channel estimation for multi user MIMO LTE networks so long as the number of users remains less than the number of training signal observations. However, as the number of users in the system becomes more than the number of observations, the system will become undetermined, and the training matrix will become ill-conditioned. The result will be poor joint channel estimation, which will significantly impair the performance of the downlink beam forming and precoding that are based on the reciprocal of the channel estimations.
What is needed, therefore, is a method and system for implementing compressed sensing joint channel estimation in a multi user MIMO LTE network.